Overview

Dataset statistics

Number of variables22
Number of observations2350
Missing cells1404
Missing cells (%)2.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory486.8 KiB
Average record size in memory212.1 B

Variable types

Categorical2
Numeric20

Alerts

Country has a high cardinality: 188 distinct valuesHigh cardinality
Life Expectancy is highly overall correlated with Adult Mortality and 12 other fieldsHigh correlation
Adult Mortality is highly overall correlated with Life Expectancy and 5 other fieldsHigh correlation
Alcohol is highly overall correlated with Income Composition Of Resources and 4 other fieldsHigh correlation
Polio is highly overall correlated with Life Expectancy and 6 other fieldsHigh correlation
Total Expenditure is highly overall correlated with Percentage ExpenditureHigh correlation
Diphtheria is highly overall correlated with Life Expectancy and 6 other fieldsHigh correlation
HIV/AIDS is highly overall correlated with Life Expectancy and 6 other fieldsHigh correlation
Thinness 10-19 Years is highly overall correlated with Life Expectancy and 6 other fieldsHigh correlation
Thinness 5-9 Years is highly overall correlated with Life Expectancy and 6 other fieldsHigh correlation
Income Composition Of Resources is highly overall correlated with Life Expectancy and 13 other fieldsHigh correlation
Schooling is highly overall correlated with Life Expectancy and 13 other fieldsHigh correlation
Population is highly overall correlated with GDP and 1 other fieldsHigh correlation
GDP is highly overall correlated with Life Expectancy and 5 other fieldsHigh correlation
Percentage Expenditure is highly overall correlated with Total ExpenditureHigh correlation
BMI is highly overall correlated with Life Expectancy and 7 other fieldsHigh correlation
Infant Deaths is highly overall correlated with Life Expectancy and 13 other fieldsHigh correlation
Under-Five Deaths is highly overall correlated with Life Expectancy and 13 other fieldsHigh correlation
Hepatitis B is highly overall correlated with Polio and 1 other fieldsHigh correlation
Measles is highly overall correlated with PopulationHigh correlation
Status is highly overall correlated with Life Expectancy and 5 other fieldsHigh correlation
Alcohol has 166 (7.1%) missing valuesMissing
Total Expenditure has 190 (8.1%) missing valuesMissing
Thinness 10-19 Years has 28 (1.2%) missing valuesMissing
Thinness 5-9 Years has 28 (1.2%) missing valuesMissing
Income Composition Of Resources has 138 (5.9%) missing valuesMissing
Schooling has 136 (5.8%) missing valuesMissing
Percentage Expenditure has 69 (2.9%) missing valuesMissing
BMI has 27 (1.1%) missing valuesMissing
Hepatitis B has 452 (19.2%) missing valuesMissing
Measles has 122 (5.2%) missing valuesMissing
Country is uniformly distributedUniform
Income Composition Of Resources has 110 (4.7%) zerosZeros
Schooling has 26 (1.1%) zerosZeros
GDP has 38 (1.6%) zerosZeros
Measles has 649 (27.6%) zerosZeros

Reproduction

Analysis started2023-01-21 04:00:35.279608
Analysis finished2023-01-21 04:01:13.753115
Duration38.47 seconds
Software versionpandas-profiling vv3.6.2
Download configurationconfig.json

Variables

Country
Categorical

HIGH CARDINALITY  UNIFORM 

Distinct188
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Memory size36.7 KiB
Bulgaria
 
16
Mexico
 
16
Guatemala
 
16
Antigua and Barbuda
 
16
Tajikistan
 
16
Other values (183)
2270 

Length

Max length30
Median length21
Mean length8.4834043
Min length4

Characters and Unicode

Total characters19936
Distinct characters56
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)0.2%

Sample

1st rowAlbania
2nd rowUruguay
3rd rowGuyana
4th rowCabo Verde
5th rowMoldova

Common Values

ValueCountFrequency (%)
Bulgaria 16
 
0.7%
Mexico 16
 
0.7%
Guatemala 16
 
0.7%
Antigua and Barbuda 16
 
0.7%
Tajikistan 16
 
0.7%
Romania 16
 
0.7%
Liberia 16
 
0.7%
Uganda 16
 
0.7%
Central African Republic 15
 
0.6%
Malaysia 15
 
0.6%
Other values (178) 2192
93.3%

Length

2023-01-20T23:01:13.810621image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
republic 63
 
2.1%
and 61
 
2.1%
south 43
 
1.5%
united 41
 
1.4%
guinea 39
 
1.3%
sudan 27
 
0.9%
new 27
 
0.9%
st 25
 
0.9%
congo 25
 
0.9%
korea 24
 
0.8%
Other values (204) 2563
87.2%

Most occurring characters

ValueCountFrequency (%)
a 3049
15.3%
i 1757
 
8.8%
n 1498
 
7.5%
e 1368
 
6.9%
r 1109
 
5.6%
o 1051
 
5.3%
u 812
 
4.1%
t 742
 
3.7%
l 693
 
3.5%
d 615
 
3.1%
Other values (46) 7242
36.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 16336
81.9%
Uppercase Letter 2905
 
14.6%
Space Separator 588
 
2.9%
Other Punctuation 81
 
0.4%
Dash Punctuation 26
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 3049
18.7%
i 1757
10.8%
n 1498
9.2%
e 1368
 
8.4%
r 1109
 
6.8%
o 1051
 
6.4%
u 812
 
5.0%
t 742
 
4.5%
l 693
 
4.2%
d 615
 
3.8%
Other values (17) 3642
22.3%
Uppercase Letter
ValueCountFrequency (%)
S 362
12.5%
B 258
 
8.9%
C 228
 
7.8%
M 223
 
7.7%
A 185
 
6.4%
G 180
 
6.2%
T 154
 
5.3%
L 151
 
5.2%
N 137
 
4.7%
I 133
 
4.6%
Other values (14) 894
30.8%
Other Punctuation
ValueCountFrequency (%)
. 53
65.4%
, 14
 
17.3%
' 14
 
17.3%
Space Separator
ValueCountFrequency (%)
588
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 26
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 19241
96.5%
Common 695
 
3.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 3049
15.8%
i 1757
 
9.1%
n 1498
 
7.8%
e 1368
 
7.1%
r 1109
 
5.8%
o 1051
 
5.5%
u 812
 
4.2%
t 742
 
3.9%
l 693
 
3.6%
d 615
 
3.2%
Other values (41) 6547
34.0%
Common
ValueCountFrequency (%)
588
84.6%
. 53
 
7.6%
- 26
 
3.7%
, 14
 
2.0%
' 14
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 19922
99.9%
None 14
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 3049
15.3%
i 1757
 
8.8%
n 1498
 
7.5%
e 1368
 
6.9%
r 1109
 
5.6%
o 1051
 
5.3%
u 812
 
4.1%
t 742
 
3.7%
l 693
 
3.5%
d 615
 
3.1%
Other values (45) 7228
36.3%
None
ValueCountFrequency (%)
ü 14
100.0%

Year
Real number (ℝ)

Distinct16
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2007.4549
Minimum2000
Maximum2015
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size36.7 KiB
2023-01-20T23:01:13.899157image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum2000
5-th percentile2000
Q12003
median2007
Q32011
95-th percentile2015
Maximum2015
Range15
Interquartile range (IQR)8

Descriptive statistics

Standard deviation4.6240859
Coefficient of variation (CV)0.0023034569
Kurtosis-1.2080397
Mean2007.4549
Median Absolute Deviation (MAD)4
Skewness0.01591154
Sum4717519
Variance21.382171
MonotonicityNot monotonic
2023-01-20T23:01:13.975246image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
2010 157
 
6.7%
2015 155
 
6.6%
2004 152
 
6.5%
2001 152
 
6.5%
2000 150
 
6.4%
2006 150
 
6.4%
2002 147
 
6.3%
2008 147
 
6.3%
2003 147
 
6.3%
2007 144
 
6.1%
Other values (6) 849
36.1%
ValueCountFrequency (%)
2000 150
6.4%
2001 152
6.5%
2002 147
6.3%
2003 147
6.3%
2004 152
6.5%
2005 142
6.0%
2006 150
6.4%
2007 144
6.1%
2008 147
6.3%
2009 142
6.0%
ValueCountFrequency (%)
2015 155
6.6%
2014 138
5.9%
2013 142
6.0%
2012 144
6.1%
2011 141
6.0%
2010 157
6.7%
2009 142
6.0%
2008 147
6.3%
2007 144
6.1%
2006 150
6.4%

Status
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size36.7 KiB
Developing
1921 
Developed
429 

Length

Max length10
Median length10
Mean length9.8174468
Min length9

Characters and Unicode

Total characters23071
Distinct characters10
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDeveloping
2nd rowDeveloping
3rd rowDeveloping
4th rowDeveloping
5th rowDeveloping

Common Values

ValueCountFrequency (%)
Developing 1921
81.7%
Developed 429
 
18.3%

Length

2023-01-20T23:01:14.075056image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-20T23:01:14.153170image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
developing 1921
81.7%
developed 429
 
18.3%

Most occurring characters

ValueCountFrequency (%)
e 5129
22.2%
D 2350
10.2%
v 2350
10.2%
l 2350
10.2%
o 2350
10.2%
p 2350
10.2%
i 1921
 
8.3%
n 1921
 
8.3%
g 1921
 
8.3%
d 429
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 20721
89.8%
Uppercase Letter 2350
 
10.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 5129
24.8%
v 2350
11.3%
l 2350
11.3%
o 2350
11.3%
p 2350
11.3%
i 1921
 
9.3%
n 1921
 
9.3%
g 1921
 
9.3%
d 429
 
2.1%
Uppercase Letter
ValueCountFrequency (%)
D 2350
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 23071
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 5129
22.2%
D 2350
10.2%
v 2350
10.2%
l 2350
10.2%
o 2350
10.2%
p 2350
10.2%
i 1921
 
8.3%
n 1921
 
8.3%
g 1921
 
8.3%
d 429
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23071
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 5129
22.2%
D 2350
10.2%
v 2350
10.2%
l 2350
10.2%
o 2350
10.2%
p 2350
10.2%
i 1921
 
8.3%
n 1921
 
8.3%
g 1921
 
8.3%
d 429
 
1.9%

Life Expectancy
Real number (ℝ)

Distinct355
Distinct (%)15.1%
Missing5
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean69.179872
Minimum36.3
Maximum89
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size36.7 KiB
2023-01-20T23:01:14.227021image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum36.3
5-th percentile51.3
Q162.9
median72
Q375.7
95-th percentile82
Maximum89
Range52.7
Interquartile range (IQR)12.8

Descriptive statistics

Standard deviation9.6122484
Coefficient of variation (CV)0.13894574
Kurtosis-0.27277621
Mean69.179872
Median Absolute Deviation (MAD)5.8
Skewness-0.62371869
Sum162226.8
Variance92.39532
MonotonicityNot monotonic
2023-01-20T23:01:14.332162image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
73 37
 
1.6%
75 26
 
1.1%
78 24
 
1.0%
74.1 22
 
0.9%
74.9 21
 
0.9%
81 21
 
0.9%
74.5 21
 
0.9%
72 20
 
0.9%
73.6 20
 
0.9%
73.5 20
 
0.9%
Other values (345) 2113
89.9%
ValueCountFrequency (%)
36.3 1
< 0.1%
39 1
< 0.1%
41 1
< 0.1%
41.5 1
< 0.1%
42.3 1
< 0.1%
43.1 1
< 0.1%
43.5 1
< 0.1%
43.8 1
< 0.1%
44 1
< 0.1%
44.3 1
< 0.1%
ValueCountFrequency (%)
89 9
0.4%
88 10
0.4%
87 8
0.3%
86 12
0.5%
85 12
0.5%
84 9
0.4%
83.7 1
 
< 0.1%
83.5 1
 
< 0.1%
83.3 1
 
< 0.1%
83.2 1
 
< 0.1%

Adult Mortality
Real number (ℝ)

Distinct407
Distinct (%)17.4%
Missing5
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean166.40128
Minimum1
Maximum723
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size36.7 KiB
2023-01-20T23:01:14.430624image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile13
Q174
median144
Q3229
95-th percentile412
Maximum723
Range722
Interquartile range (IQR)155

Descriptive statistics

Standard deviation124.76766
Coefficient of variation (CV)0.74979989
Kurtosis1.7432704
Mean166.40128
Median Absolute Deviation (MAD)75
Skewness1.1804716
Sum390211
Variance15566.969
MonotonicityNot monotonic
2023-01-20T23:01:14.525418image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12 28
 
1.2%
14 25
 
1.1%
16 21
 
0.9%
138 21
 
0.9%
13 19
 
0.8%
134 19
 
0.8%
165 18
 
0.8%
157 17
 
0.7%
76 17
 
0.7%
144 16
 
0.7%
Other values (397) 2144
91.2%
ValueCountFrequency (%)
1 10
0.4%
2 8
0.3%
3 4
 
0.2%
4 3
 
0.1%
5 2
 
0.1%
6 9
0.4%
7 10
0.4%
8 12
0.5%
9 7
0.3%
11 15
0.6%
ValueCountFrequency (%)
723 1
< 0.1%
717 1
< 0.1%
715 1
< 0.1%
699 1
< 0.1%
693 1
< 0.1%
686 1
< 0.1%
682 1
< 0.1%
675 1
< 0.1%
654 1
< 0.1%
652 1
< 0.1%

Alcohol
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct984
Distinct (%)45.1%
Missing166
Missing (%)7.1%
Infinite0
Infinite (%)0.0%
Mean4.7391255
Minimum0.01
Maximum17.87
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size36.7 KiB
2023-01-20T23:01:14.630040image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile0.01
Q11.0175
median3.9
Q38.01
95-th percentile12.05
Maximum17.87
Range17.86
Interquartile range (IQR)6.9925

Descriptive statistics

Standard deviation4.0971148
Coefficient of variation (CV)0.86452972
Kurtosis-0.83211737
Mean4.7391255
Median Absolute Deviation (MAD)3.34
Skewness0.56309865
Sum10350.25
Variance16.78635
MonotonicityNot monotonic
2023-01-20T23:01:14.719795image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.01 208
 
8.9%
0.03 12
 
0.5%
0.02 10
 
0.4%
0.56 9
 
0.4%
0.09 9
 
0.4%
0.49 8
 
0.3%
1.18 8
 
0.3%
0.21 8
 
0.3%
1.29 8
 
0.3%
0.06 8
 
0.3%
Other values (974) 1896
80.7%
(Missing) 166
 
7.1%
ValueCountFrequency (%)
0.01 208
8.9%
0.02 10
 
0.4%
0.03 12
 
0.5%
0.04 7
 
0.3%
0.05 7
 
0.3%
0.06 8
 
0.3%
0.07 3
 
0.1%
0.08 6
 
0.3%
0.09 9
 
0.4%
0.1 4
 
0.2%
ValueCountFrequency (%)
17.87 1
< 0.1%
17.31 1
< 0.1%
16.99 1
< 0.1%
16.58 1
< 0.1%
16.35 1
< 0.1%
15.52 1
< 0.1%
15.19 1
< 0.1%
15.14 1
< 0.1%
15.07 1
< 0.1%
15.04 2
0.1%

Polio
Real number (ℝ)

Distinct73
Distinct (%)3.1%
Missing17
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean82.731247
Minimum3
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size36.7 KiB
2023-01-20T23:01:14.835237image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile9
Q178
median93
Q397
95-th percentile99
Maximum99
Range96
Interquartile range (IQR)19

Descriptive statistics

Standard deviation23.081076
Coefficient of variation (CV)0.27898861
Kurtosis3.903596
Mean82.731247
Median Absolute Deviation (MAD)6
Skewness-2.1111678
Sum193012
Variance532.73606
MonotonicityNot monotonic
2023-01-20T23:01:14.966346image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
99 298
 
12.7%
98 207
 
8.8%
97 167
 
7.1%
96 156
 
6.6%
95 147
 
6.3%
94 129
 
5.5%
93 91
 
3.9%
92 80
 
3.4%
91 79
 
3.4%
88 53
 
2.3%
Other values (63) 926
39.4%
ValueCountFrequency (%)
3 6
 
0.3%
4 9
 
0.4%
5 7
 
0.3%
6 8
 
0.3%
7 17
 
0.7%
8 30
1.3%
9 52
2.2%
17 1
 
< 0.1%
23 1
 
< 0.1%
24 2
 
0.1%
ValueCountFrequency (%)
99 298
12.7%
98 207
8.8%
97 167
7.1%
96 156
6.6%
95 147
6.3%
94 129
5.5%
93 91
 
3.9%
92 80
 
3.4%
91 79
 
3.4%
89 45
 
1.9%

Total Expenditure
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct772
Distinct (%)35.7%
Missing190
Missing (%)8.1%
Infinite0
Infinite (%)0.0%
Mean5.9579167
Minimum0.65
Maximum17.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size36.7 KiB
2023-01-20T23:01:15.098670image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.65
5-th percentile1.959
Q14.28
median5.78
Q37.5
95-th percentile9.76
Maximum17.6
Range16.95
Interquartile range (IQR)3.22

Descriptive statistics

Standard deviation2.4841337
Coefficient of variation (CV)0.4169467
Kurtosis1.1242021
Mean5.9579167
Median Absolute Deviation (MAD)1.58
Skewness0.59921062
Sum12869.1
Variance6.1709201
MonotonicityNot monotonic
2023-01-20T23:01:15.210422image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.1 9
 
0.4%
4.7 9
 
0.4%
4.6 9
 
0.4%
5.6 9
 
0.4%
5.25 9
 
0.4%
5.17 9
 
0.4%
5.3 9
 
0.4%
6.7 9
 
0.4%
5.9 9
 
0.4%
4.41 8
 
0.3%
Other values (762) 2071
88.1%
(Missing) 190
 
8.1%
ValueCountFrequency (%)
0.65 1
 
< 0.1%
0.74 1
 
< 0.1%
0.76 1
 
< 0.1%
1.1 2
0.1%
1.12 2
0.1%
1.15 2
0.1%
1.17 2
0.1%
1.18 2
0.1%
1.19 3
0.1%
1.2 2
0.1%
ValueCountFrequency (%)
17.6 1
< 0.1%
17.24 1
< 0.1%
17.2 2
0.1%
17.14 1
< 0.1%
17 1
< 0.1%
16.2 1
< 0.1%
15.6 1
< 0.1%
15.27 1
< 0.1%
15.15 1
< 0.1%
14.55 1
< 0.1%

Diphtheria
Real number (ℝ)

Distinct81
Distinct (%)3.5%
Missing17
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean82.168881
Minimum2
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size36.7 KiB
2023-01-20T23:01:15.322042image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile9
Q178
median93
Q397
95-th percentile99
Maximum99
Range97
Interquartile range (IQR)19

Descriptive statistics

Standard deviation23.937718
Coefficient of variation (CV)0.2913234
Kurtosis3.464934
Mean82.168881
Median Absolute Deviation (MAD)5
Skewness-2.0580538
Sum191700
Variance573.01435
MonotonicityNot monotonic
2023-01-20T23:01:15.424477image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
99 275
 
11.7%
98 206
 
8.8%
97 170
 
7.2%
95 158
 
6.7%
96 154
 
6.6%
94 118
 
5.0%
93 92
 
3.9%
91 81
 
3.4%
92 80
 
3.4%
89 62
 
2.6%
Other values (71) 937
39.9%
ValueCountFrequency (%)
2 1
 
< 0.1%
3 4
 
0.2%
4 12
 
0.5%
5 8
 
0.3%
6 12
 
0.5%
7 17
 
0.7%
8 34
1.4%
9 52
2.2%
16 1
 
< 0.1%
19 1
 
< 0.1%
ValueCountFrequency (%)
99 275
11.7%
98 206
8.8%
97 170
7.2%
96 154
6.6%
95 158
6.7%
94 118
5.0%
93 92
 
3.9%
92 80
 
3.4%
91 81
 
3.4%
89 62
 
2.6%

HIV/AIDS
Real number (ℝ)

Distinct183
Distinct (%)7.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.784
Minimum0.1
Maximum50.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size36.7 KiB
2023-01-20T23:01:15.528087image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.1
Q10.1
median0.1
Q30.8
95-th percentile9.055
Maximum50.6
Range50.5
Interquartile range (IQR)0.7

Descriptive statistics

Standard deviation5.1063545
Coefficient of variation (CV)2.8623064
Kurtosis33.036016
Mean1.784
Median Absolute Deviation (MAD)0
Skewness5.2368558
Sum4192.4
Variance26.074857
MonotonicityNot monotonic
2023-01-20T23:01:15.639667image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1 1417
60.3%
0.2 103
 
4.4%
0.3 87
 
3.7%
0.4 56
 
2.4%
0.5 31
 
1.3%
0.8 28
 
1.2%
0.6 27
 
1.1%
0.9 26
 
1.1%
0.7 21
 
0.9%
1.9 18
 
0.8%
Other values (173) 536
 
22.8%
ValueCountFrequency (%)
0.1 1417
60.3%
0.2 103
 
4.4%
0.3 87
 
3.7%
0.4 56
 
2.4%
0.5 31
 
1.3%
0.6 27
 
1.1%
0.7 21
 
0.9%
0.8 28
 
1.2%
0.9 26
 
1.1%
1 8
 
0.3%
ValueCountFrequency (%)
50.6 1
< 0.1%
49.9 1
< 0.1%
49.1 1
< 0.1%
48.8 1
< 0.1%
46.4 1
< 0.1%
43.7 1
< 0.1%
42.1 1
< 0.1%
40.2 1
< 0.1%
39.8 1
< 0.1%
38.8 1
< 0.1%

Thinness 10-19 Years
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct190
Distinct (%)8.2%
Missing28
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean4.7798019
Minimum0.1
Maximum27.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size36.7 KiB
2023-01-20T23:01:15.754400image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.6
Q11.5
median3.3
Q37.1
95-th percentile13.595
Maximum27.7
Range27.6
Interquartile range (IQR)5.6

Descriptive statistics

Standard deviation4.3857532
Coefficient of variation (CV)0.91755961
Kurtosis4.3129781
Mean4.7798019
Median Absolute Deviation (MAD)2.3
Skewness1.7549158
Sum11098.7
Variance19.234831
MonotonicityNot monotonic
2023-01-20T23:01:15.859845image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 68
 
2.9%
1.9 56
 
2.4%
1.5 53
 
2.3%
0.8 52
 
2.2%
0.7 50
 
2.1%
1.7 48
 
2.0%
1.2 48
 
2.0%
1.1 46
 
2.0%
2 46
 
2.0%
1.6 44
 
1.9%
Other values (180) 1811
77.1%
ValueCountFrequency (%)
0.1 22
 
0.9%
0.2 33
1.4%
0.3 26
 
1.1%
0.4 4
 
0.2%
0.5 29
1.2%
0.6 35
1.5%
0.7 50
2.1%
0.8 52
2.2%
0.9 43
1.8%
1 68
2.9%
ValueCountFrequency (%)
27.7 1
< 0.1%
27.5 1
< 0.1%
27.4 1
< 0.1%
27.3 1
< 0.1%
27.2 1
< 0.1%
27.1 2
0.1%
27 2
0.1%
26.9 2
0.1%
26.8 2
0.1%
26.7 1
< 0.1%

Thinness 5-9 Years
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct194
Distinct (%)8.4%
Missing28
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean4.8356589
Minimum0.1
Maximum28.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size36.7 KiB
2023-01-20T23:01:15.964196image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.5
Q11.5
median3.3
Q37.2
95-th percentile13.7
Maximum28.6
Range28.5
Interquartile range (IQR)5.7

Descriptive statistics

Standard deviation4.5019632
Coefficient of variation (CV)0.9309927
Kurtosis4.6436919
Mean4.8356589
Median Absolute Deviation (MAD)2.3
Skewness1.81682
Sum11228.4
Variance20.267672
MonotonicityNot monotonic
2023-01-20T23:01:16.066651image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.9 59
 
2.5%
1 54
 
2.3%
1.1 53
 
2.3%
1.9 53
 
2.3%
0.5 52
 
2.2%
1.3 47
 
2.0%
0.6 47
 
2.0%
2.1 45
 
1.9%
1.7 44
 
1.9%
1.4 44
 
1.9%
Other values (184) 1824
77.6%
ValueCountFrequency (%)
0.1 28
1.2%
0.2 37
1.6%
0.3 21
 
0.9%
0.4 13
 
0.6%
0.5 52
2.2%
0.6 47
2.0%
0.7 36
1.5%
0.8 27
1.1%
0.9 59
2.5%
1 54
2.3%
ValueCountFrequency (%)
28.6 1
< 0.1%
28.5 1
< 0.1%
28.4 1
< 0.1%
28.3 1
< 0.1%
28.1 1
< 0.1%
28 2
0.1%
27.8 2
0.1%
27.7 1
< 0.1%
27.6 1
< 0.1%
27.5 1
< 0.1%

Income Composition Of Resources
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct598
Distinct (%)27.0%
Missing138
Missing (%)5.9%
Infinite0
Infinite (%)0.0%
Mean0.62645479
Minimum0
Maximum0.948
Zeros110
Zeros (%)4.7%
Negative0
Negative (%)0.0%
Memory size36.7 KiB
2023-01-20T23:01:16.173946image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.2541
Q10.494
median0.677
Q30.781
95-th percentile0.892
Maximum0.948
Range0.948
Interquartile range (IQR)0.287

Descriptive statistics

Standard deviation0.21388009
Coefficient of variation (CV)0.34141345
Kurtosis1.3499079
Mean0.62645479
Median Absolute Deviation (MAD)0.128
Skewness-1.1507686
Sum1385.718
Variance0.045744694
MonotonicityNot monotonic
2023-01-20T23:01:16.269035image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 110
 
4.7%
0.7 14
 
0.6%
0.739 12
 
0.5%
0.636 11
 
0.5%
0.714 10
 
0.4%
0.877 9
 
0.4%
0.86 9
 
0.4%
0.797 9
 
0.4%
0.723 9
 
0.4%
0.706 9
 
0.4%
Other values (588) 2010
85.5%
(Missing) 138
 
5.9%
ValueCountFrequency (%)
0 110
4.7%
0.253 1
 
< 0.1%
0.255 1
 
< 0.1%
0.266 1
 
< 0.1%
0.268 3
 
0.1%
0.27 1
 
< 0.1%
0.276 1
 
< 0.1%
0.278 1
 
< 0.1%
0.279 1
 
< 0.1%
0.283 1
 
< 0.1%
ValueCountFrequency (%)
0.948 1
 
< 0.1%
0.945 1
 
< 0.1%
0.942 1
 
< 0.1%
0.941 1
 
< 0.1%
0.939 1
 
< 0.1%
0.937 1
 
< 0.1%
0.936 5
0.2%
0.934 2
 
0.1%
0.933 1
 
< 0.1%
0.932 1
 
< 0.1%

Schooling
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct169
Distinct (%)7.6%
Missing136
Missing (%)5.8%
Infinite0
Infinite (%)0.0%
Mean11.993225
Minimum0
Maximum20.7
Zeros26
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size36.7 KiB
2023-01-20T23:01:16.363747image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5.665
Q110.1
median12.3
Q314.3
95-th percentile16.8
Maximum20.7
Range20.7
Interquartile range (IQR)4.2

Descriptive statistics

Standard deviation3.4113695
Coefficient of variation (CV)0.28444138
Kurtosis0.9719408
Mean11.993225
Median Absolute Deviation (MAD)2.1
Skewness-0.62393212
Sum26553
Variance11.637442
MonotonicityNot monotonic
2023-01-20T23:01:16.453473image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12.9 49
 
2.1%
12.5 41
 
1.7%
12.8 40
 
1.7%
13.3 37
 
1.6%
12.6 35
 
1.5%
11.9 34
 
1.4%
10.7 33
 
1.4%
12.3 33
 
1.4%
11.7 32
 
1.4%
15.8 31
 
1.3%
Other values (159) 1849
78.7%
(Missing) 136
 
5.8%
ValueCountFrequency (%)
0 26
1.1%
2.8 1
 
< 0.1%
2.9 3
 
0.1%
3 1
 
< 0.1%
3.1 1
 
< 0.1%
3.5 2
 
0.1%
3.6 1
 
< 0.1%
3.7 2
 
0.1%
3.8 2
 
0.1%
3.9 2
 
0.1%
ValueCountFrequency (%)
20.7 1
 
< 0.1%
20.5 1
 
< 0.1%
20.4 3
0.1%
20.3 4
0.2%
20.1 2
 
0.1%
19.8 1
 
< 0.1%
19.7 1
 
< 0.1%
19.5 3
0.1%
19.3 2
 
0.1%
19.2 5
0.2%

Population
Real number (ℝ)

Distinct2349
Distinct (%)100.0%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean36517784
Minimum10694
Maximum1.37986 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size36.7 KiB
2023-01-20T23:01:16.550723image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum10694
5-th percentile182886
Q12175425
median8171966
Q325782029
95-th percentile1.275554 × 108
Maximum1.37986 × 109
Range1.3798493 × 109
Interquartile range (IQR)23606604

Descriptive statistics

Standard deviation1.3396794 × 108
Coefficient of variation (CV)3.6685672
Kurtosis73.983276
Mean36517784
Median Absolute Deviation (MAD)7289080
Skewness8.3402681
Sum8.5780275 × 1010
Variance1.794741 × 1016
MonotonicityNot monotonic
2023-01-20T23:01:16.649082image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2970017 1
 
< 0.1%
11229387 1
 
< 0.1%
92400 1
 
< 0.1%
883083 1
 
< 0.1%
19028802 1
 
< 0.1%
20162340 1
 
< 0.1%
16602651 1
 
< 0.1%
9704287 1
 
< 0.1%
4179350 1
 
< 0.1%
5312437 1
 
< 0.1%
Other values (2339) 2339
99.5%
ValueCountFrequency (%)
10694 1
< 0.1%
17805 1
< 0.1%
35425 1
< 0.1%
51352 1
< 0.1%
75055 1
< 0.1%
76215 1
< 0.1%
77195 1
< 0.1%
78075 1
< 0.1%
78941 1
< 0.1%
79869 1
< 0.1%
ValueCountFrequency (%)
1379860000 1
< 0.1%
1371860000 1
< 0.1%
1363240000 1
< 0.1%
1354190000 1
< 0.1%
1345035000 1
< 0.1%
1337705000 1
< 0.1%
1331260000 1
< 0.1%
1324655000 1
< 0.1%
1322866505 1
< 0.1%
1317885000 1
< 0.1%

GDP
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct2312
Distinct (%)98.4%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean3.16707 × 1011
Minimum0
Maximum1.8206 × 1013
Zeros38
Zeros (%)1.6%
Negative0
Negative (%)0.0%
Memory size36.7 KiB
2023-01-20T23:01:16.747918image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4.9423294 × 108
Q14.7140722 × 109
median2.0289628 × 1010
Q31.37244 × 1011
95-th percentile1.448306 × 1012
Maximum1.8206 × 1013
Range1.8206 × 1013
Interquartile range (IQR)1.3252993 × 1011

Descriptive statistics

Standard deviation1.2771143 × 1012
Coefficient of variation (CV)4.032479
Kurtosis92.923588
Mean3.16707 × 1011
Median Absolute Deviation (MAD)1.937437 × 1010
Skewness8.8219407
Sum7.4394475 × 1014
Variance1.631021 × 1024
MonotonicityNot monotonic
2023-01-20T23:01:16.843746image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 38
 
1.6%
1.067732414 × 10101
 
< 0.1%
2.95087 × 10111
 
< 0.1%
3076305453 1
 
< 0.1%
4.15851 × 10111
 
< 0.1%
5.238100689 × 10101
 
< 0.1%
1.208029664 × 10101
 
< 0.1%
6013184809 1
 
< 0.1%
5567405605 1
 
< 0.1%
2.150206146 × 10101
 
< 0.1%
Other values (2302) 2302
98.0%
ValueCountFrequency (%)
0 38
1.6%
63101272.37 1
 
< 0.1%
67254174.4 1
 
< 0.1%
72196457.68 1
 
< 0.1%
85171308.17 1
 
< 0.1%
90231856.8 1
 
< 0.1%
98491843.64 1
 
< 0.1%
102085583.5 1
 
< 0.1%
102367039.3 1
 
< 0.1%
114582561.6 1
 
< 0.1%
ValueCountFrequency (%)
1.8206 × 10131
< 0.1%
1.75507 × 10131
< 0.1%
1.6254 × 10131
< 0.1%
1.55997 × 10131
< 0.1%
1.5049 × 10131
< 0.1%
1.47699 × 10131
< 0.1%
1.44781 × 10131
< 0.1%
1.38156 × 10131
< 0.1%
1.30392 × 10131
< 0.1%
1.14564 × 10131
< 0.1%

Percentage Expenditure
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct861
Distinct (%)37.7%
Missing69
Missing (%)2.9%
Infinite0
Infinite (%)0.0%
Mean5.9870671
Minimum1.26
Maximum20.41
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size36.7 KiB
2023-01-20T23:01:16.943741image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1.26
5-th percentile2.55
Q14.13
median5.52
Q37.72
95-th percentile10.23
Maximum20.41
Range19.15
Interquartile range (IQR)3.59

Descriptive statistics

Standard deviation2.4869015
Coefficient of variation (CV)0.41537892
Kurtosis0.92610314
Mean5.9870671
Median Absolute Deviation (MAD)1.7
Skewness0.75543855
Sum13656.5
Variance6.184679
MonotonicityNot monotonic
2023-01-20T23:01:17.034879image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.76 12
 
0.5%
5.02 10
 
0.4%
5.08 9
 
0.4%
4.65 9
 
0.4%
5.22 8
 
0.3%
4.43 8
 
0.3%
4.44 8
 
0.3%
5.28 8
 
0.3%
4.47 8
 
0.3%
5.29 8
 
0.3%
Other values (851) 2193
93.3%
(Missing) 69
 
2.9%
ValueCountFrequency (%)
1.26 1
< 0.1%
1.51 1
< 0.1%
1.52 1
< 0.1%
1.55 2
0.1%
1.57 1
< 0.1%
1.6 1
< 0.1%
1.66 1
< 0.1%
1.7 1
< 0.1%
1.75 2
0.1%
1.82 1
< 0.1%
ValueCountFrequency (%)
20.41 1
< 0.1%
17.73 1
< 0.1%
16.52 1
< 0.1%
16.26 1
< 0.1%
16.25 1
< 0.1%
16.23 1
< 0.1%
16.2 1
< 0.1%
16.18 1
< 0.1%
15.27 1
< 0.1%
15.13 1
< 0.1%

BMI
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct2323
Distinct (%)100.0%
Missing27
Missing (%)1.1%
Infinite0
Infinite (%)0.0%
Mean50.396512
Minimum39.729504
Maximum65.78269
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size36.7 KiB
2023-01-20T23:01:17.133447image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum39.729504
5-th percentile42.802921
Q146.855045
median51.358675
Q353.218797
95-th percentile57.056138
Maximum65.78269
Range26.053186
Interquartile range (IQR)6.3637515

Descriptive statistics

Standard deviation4.488411
Coefficient of variation (CV)0.089061938
Kurtosis-0.051216427
Mean50.396512
Median Absolute Deviation (MAD)2.8196631
Skewness-0.034733399
Sum117071.1
Variance20.145834
MonotonicityNot monotonic
2023-01-20T23:01:17.231139image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
54.85824827 1
 
< 0.1%
44.26541225 1
 
< 0.1%
44.60697812 1
 
< 0.1%
58.11596844 1
 
< 0.1%
53.98840553 1
 
< 0.1%
52.7578119 1
 
< 0.1%
44.84115766 1
 
< 0.1%
43.805073 1
 
< 0.1%
44.3081342 1
 
< 0.1%
49.83333758 1
 
< 0.1%
Other values (2313) 2313
98.4%
(Missing) 27
 
1.1%
ValueCountFrequency (%)
39.72950381 1
< 0.1%
39.84934406 1
< 0.1%
39.93079504 1
< 0.1%
39.96379172 1
< 0.1%
40.11504066 1
< 0.1%
40.17793102 1
< 0.1%
40.30733255 1
< 0.1%
40.35296874 1
< 0.1%
40.36906543 1
< 0.1%
40.45866886 1
< 0.1%
ValueCountFrequency (%)
65.78268964 1
< 0.1%
65.58608482 1
< 0.1%
64.74180289 1
< 0.1%
64.61432124 1
< 0.1%
64.51581414 1
< 0.1%
64.49665805 1
< 0.1%
64.37972883 1
< 0.1%
64.29140071 1
< 0.1%
64.2638291 1
< 0.1%
64.14947373 1
< 0.1%

Infant Deaths
Real number (ℝ)

Distinct794
Distinct (%)33.8%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean30.624819
Minimum1.8
Maximum138.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size36.7 KiB
2023-01-20T23:01:17.328470image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1.8
5-th percentile3.2
Q17.9
median19.6
Q348.5
95-th percentile87.46
Maximum138.1
Range136.3
Interquartile range (IQR)40.6

Descriptive statistics

Standard deviation27.803616
Coefficient of variation (CV)0.90787855
Kurtosis0.31973368
Mean30.624819
Median Absolute Deviation (MAD)14.7
Skewness1.0806411
Sum71937.7
Variance773.04107
MonotonicityNot monotonic
2023-01-20T23:01:17.435208image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.3 24
 
1.0%
3.5 21
 
0.9%
3.4 18
 
0.8%
4.3 18
 
0.8%
3.7 16
 
0.7%
4.7 16
 
0.7%
3.6 16
 
0.7%
3.8 15
 
0.6%
4.1 14
 
0.6%
6.9 14
 
0.6%
Other values (784) 2177
92.6%
ValueCountFrequency (%)
1.8 1
 
< 0.1%
1.9 2
 
0.1%
2 3
 
0.1%
2.1 2
 
0.1%
2.2 10
0.4%
2.3 12
0.5%
2.4 14
0.6%
2.5 11
0.5%
2.6 9
0.4%
2.7 8
0.3%
ValueCountFrequency (%)
138.1 1
< 0.1%
135.6 1
< 0.1%
132.9 1
< 0.1%
130.2 1
< 0.1%
127.9 1
< 0.1%
127.2 1
< 0.1%
119.7 1
< 0.1%
118.2 1
< 0.1%
117.6 1
< 0.1%
114.5 1
< 0.1%

Under-Five Deaths
Real number (ℝ)

Distinct794
Distinct (%)33.8%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean30.624819
Minimum1.8
Maximum138.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size36.7 KiB
2023-01-20T23:01:17.534286image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1.8
5-th percentile3.2
Q17.9
median19.6
Q348.5
95-th percentile87.46
Maximum138.1
Range136.3
Interquartile range (IQR)40.6

Descriptive statistics

Standard deviation27.803616
Coefficient of variation (CV)0.90787855
Kurtosis0.31973368
Mean30.624819
Median Absolute Deviation (MAD)14.7
Skewness1.0806411
Sum71937.7
Variance773.04107
MonotonicityNot monotonic
2023-01-20T23:01:17.630975image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.3 24
 
1.0%
3.5 21
 
0.9%
3.4 18
 
0.8%
4.3 18
 
0.8%
3.7 16
 
0.7%
4.7 16
 
0.7%
3.6 16
 
0.7%
3.8 15
 
0.6%
4.1 14
 
0.6%
6.9 14
 
0.6%
Other values (784) 2177
92.6%
ValueCountFrequency (%)
1.8 1
 
< 0.1%
1.9 2
 
0.1%
2 3
 
0.1%
2.1 2
 
0.1%
2.2 10
0.4%
2.3 12
0.5%
2.4 14
0.6%
2.5 11
0.5%
2.6 9
0.4%
2.7 8
0.3%
ValueCountFrequency (%)
138.1 1
< 0.1%
135.6 1
< 0.1%
132.9 1
< 0.1%
130.2 1
< 0.1%
127.9 1
< 0.1%
127.2 1
< 0.1%
119.7 1
< 0.1%
118.2 1
< 0.1%
117.6 1
< 0.1%
114.5 1
< 0.1%

Hepatitis B
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct87
Distinct (%)4.6%
Missing452
Missing (%)19.2%
Infinite0
Infinite (%)0.0%
Mean85.254478
Minimum5
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size36.7 KiB
2023-01-20T23:01:17.731506image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile46
Q180
median92
Q397
95-th percentile99
Maximum99
Range94
Interquartile range (IQR)17

Descriptive statistics

Standard deviation17.109035
Coefficient of variation (CV)0.20068195
Kurtosis4.39266
Mean85.254478
Median Absolute Deviation (MAD)6
Skewness-2.0400703
Sum161813
Variance292.71908
MonotonicityNot monotonic
2023-01-20T23:01:18.286467image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
99 185
 
7.9%
98 172
 
7.3%
96 140
 
6.0%
97 120
 
5.1%
95 120
 
5.1%
94 97
 
4.1%
93 77
 
3.3%
92 76
 
3.2%
91 69
 
2.9%
88 53
 
2.3%
Other values (77) 789
33.6%
(Missing) 452
19.2%
ValueCountFrequency (%)
5 2
 
0.1%
6 2
 
0.1%
7 2
 
0.1%
8 1
 
< 0.1%
10 1
 
< 0.1%
14 7
0.3%
15 1
 
< 0.1%
17 2
 
0.1%
18 1
 
< 0.1%
20 2
 
0.1%
ValueCountFrequency (%)
99 185
7.9%
98 172
7.3%
97 120
5.1%
96 140
6.0%
95 120
5.1%
94 97
4.1%
93 77
3.3%
92 76
3.2%
91 69
 
2.9%
90 52
 
2.2%

Measles
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct811
Distinct (%)36.4%
Missing122
Missing (%)5.2%
Infinite0
Infinite (%)0.0%
Mean2404.7572
Minimum0
Maximum212183
Zeros649
Zeros (%)27.6%
Negative0
Negative (%)0.0%
Memory size36.7 KiB
2023-01-20T23:01:18.382345image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median24
Q3421.25
95-th percentile9974.95
Maximum212183
Range212183
Interquartile range (IQR)421.25

Descriptive statistics

Standard deviation11463.594
Coefficient of variation (CV)4.7670484
Kurtosis133.75115
Mean2404.7572
Median Absolute Deviation (MAD)24
Skewness10.196583
Sum5357799
Variance1.3141398 × 108
MonotonicityNot monotonic
2023-01-20T23:01:18.471178image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 649
27.6%
1 82
 
3.5%
2 60
 
2.6%
3 33
 
1.4%
4 27
 
1.1%
6 23
 
1.0%
7 22
 
0.9%
5 19
 
0.8%
8 19
 
0.8%
11 19
 
0.8%
Other values (801) 1275
54.3%
(Missing) 122
 
5.2%
ValueCountFrequency (%)
0 649
27.6%
1 82
 
3.5%
2 60
 
2.6%
3 33
 
1.4%
4 27
 
1.1%
5 19
 
0.8%
6 23
 
1.0%
7 22
 
0.9%
8 19
 
0.8%
9 18
 
0.8%
ValueCountFrequency (%)
212183 1
< 0.1%
182485 1
< 0.1%
168107 1
< 0.1%
141258 1
< 0.1%
133802 1
< 0.1%
131441 1
< 0.1%
110927 1
< 0.1%
109023 1
< 0.1%
88381 1
< 0.1%
80123 1
< 0.1%

Interactions

2023-01-20T23:01:11.209363image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-20T23:00:35.875291image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-20T23:00:37.823436image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-20T23:00:39.462510image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-20T23:00:41.277086image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-20T23:00:43.227158image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-20T23:00:44.947078image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-20T23:00:46.948746image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-20T23:00:48.696091image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-20T23:00:50.441966image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-20T23:00:52.491721image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-20T23:00:54.335494image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-20T23:00:56.094908image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-20T23:00:57.712291image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-20T23:00:59.808475image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-20T23:01:01.739778image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-20T23:01:03.709485image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-20T23:01:05.516525image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
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2023-01-20T23:01:11.300381image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
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2023-01-20T23:00:37.903294image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
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2023-01-20T23:00:45.035319image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-20T23:00:47.037543image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
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2023-01-20T23:00:50.533019image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-20T23:00:52.586475image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-20T23:00:54.430910image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-20T23:00:56.180044image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-20T23:00:57.822944image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-20T23:00:59.894651image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-20T23:01:01.832763image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-20T23:01:03.793888image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-20T23:01:05.617794image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-20T23:01:07.433394image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-20T23:01:09.586054image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-20T23:01:11.379782image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-20T23:00:36.048052image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
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2023-01-20T23:00:41.631354image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-20T23:00:43.402880image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
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2023-01-20T23:00:48.869529image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
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2023-01-20T23:00:52.676372image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-20T23:00:54.518803image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-20T23:00:56.256562image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-20T23:00:57.907454image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-20T23:00:59.977942image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
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2023-01-20T23:01:11.459644image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-20T23:00:36.134922image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-20T23:00:38.060630image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-20T23:00:39.736192image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-20T23:00:41.718164image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-20T23:00:43.489559image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-20T23:00:45.237769image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
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2023-01-20T23:00:54.609470image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
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2023-01-20T23:00:41.808976image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
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2023-01-20T23:00:56.435331image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
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2023-01-20T23:01:00.242709image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
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2023-01-20T23:00:36.308543image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-20T23:00:38.219705image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-20T23:00:39.915972image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-20T23:00:41.894732image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-20T23:00:43.647621image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-20T23:00:45.413385image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-20T23:00:47.368402image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-20T23:00:49.137967image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-20T23:00:50.879383image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-20T23:00:52.950864image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-20T23:00:54.775112image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-20T23:00:56.511990image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-20T23:00:58.170065image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-20T23:01:00.338556image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-20T23:01:02.227253image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-20T23:01:04.147958image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-20T23:01:05.994056image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-20T23:01:07.780118image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-20T23:01:09.898969image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-20T23:01:11.778286image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-20T23:00:36.393534image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-20T23:00:38.303355image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-20T23:00:40.007967image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-20T23:00:41.996747image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-20T23:00:43.738791image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-20T23:00:45.498587image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-20T23:00:47.498538image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-20T23:00:49.235955image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
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2023-01-20T23:00:53.966854image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-20T23:00:55.751935image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-20T23:00:57.382879image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-20T23:00:59.182238image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-20T23:01:01.369383image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-20T23:01:03.351120image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-20T23:01:05.168483image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-20T23:01:07.002714image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-20T23:01:09.132099image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-20T23:01:10.871146image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-20T23:01:12.725315image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-20T23:00:37.411439image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-20T23:00:39.215736image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-20T23:00:41.028924image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-20T23:00:42.975244image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-20T23:00:44.675311image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-20T23:00:46.693757image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-20T23:00:48.453580image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-20T23:00:50.180272image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-20T23:00:51.977167image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-20T23:00:54.067987image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-20T23:00:55.835526image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-20T23:00:57.464737image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-20T23:00:59.268308image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-20T23:01:01.477019image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-20T23:01:03.461674image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-20T23:01:05.266520image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-20T23:01:07.095524image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-20T23:01:09.228342image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-20T23:01:10.949318image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-20T23:01:12.808525image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-20T23:00:37.496002image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-20T23:00:39.300953image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-20T23:00:41.117305image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-20T23:00:43.065691image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-20T23:00:44.772396image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-20T23:00:46.788668image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-20T23:00:48.540860image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-20T23:00:50.271198image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-20T23:00:52.321920image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-20T23:00:54.167483image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-20T23:00:55.927636image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-20T23:00:57.551327image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-20T23:00:59.651573image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-20T23:01:01.582105image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-20T23:01:03.548720image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-20T23:01:05.350184image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-20T23:01:07.185380image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-20T23:01:09.332018image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-20T23:01:11.035540image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-20T23:01:12.897617image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-20T23:00:37.569692image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-20T23:00:39.372089image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-20T23:00:41.194701image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-20T23:00:43.146040image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-20T23:00:44.871112image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-20T23:00:46.867004image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-20T23:00:48.617699image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-20T23:00:50.358194image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-20T23:00:52.408765image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-20T23:00:54.253022image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-20T23:00:56.006572image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-20T23:00:57.631719image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-20T23:00:59.729166image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-20T23:01:01.660919image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-20T23:01:03.630108image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-20T23:01:05.432320image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-20T23:01:07.264280image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-20T23:01:09.408814image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-20T23:01:11.113460image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Correlations

2023-01-20T23:01:18.561808image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
YearLife ExpectancyAdult MortalityAlcoholPolioTotal ExpenditureDiphtheriaHIV/AIDSThinness 10-19 YearsThinness 5-9 YearsIncome Composition Of ResourcesSchoolingPopulationGDPPercentage ExpenditureBMIInfant DeathsUnder-Five DeathsHepatitis BMeaslesStatus
Year1.0000.160-0.056-0.0880.0870.0780.114-0.049-0.034-0.0380.1980.1950.0500.1930.0950.187-0.148-0.1480.085-0.1100.000
Life Expectancy0.1601.000-0.6550.4490.5360.2880.545-0.762-0.614-0.6250.8700.815-0.0200.5590.4070.592-0.927-0.9270.364-0.2890.627
Adult Mortality-0.056-0.6551.000-0.219-0.323-0.161-0.3290.5280.3970.408-0.562-0.5070.015-0.364-0.252-0.3860.6000.600-0.2380.1430.371
Alcohol-0.0880.449-0.2191.0000.2710.3460.288-0.216-0.464-0.4590.5210.568-0.0180.3440.4430.285-0.548-0.5480.109-0.2280.675
Polio0.0870.536-0.3230.2711.0000.1420.926-0.494-0.218-0.2290.5140.512-0.0650.2600.1940.334-0.591-0.5910.770-0.2810.304
Total Expenditure0.0780.288-0.1610.3460.1421.0000.157-0.137-0.366-0.3830.2230.295-0.0280.1070.6370.246-0.300-0.3000.065-0.1970.415
Diphtheria0.1140.545-0.3290.2880.9260.1571.000-0.480-0.228-0.2370.5200.521-0.0580.2900.1890.345-0.601-0.6010.786-0.2740.315
HIV/AIDS-0.049-0.7620.528-0.216-0.494-0.137-0.4801.0000.4880.470-0.658-0.6230.029-0.387-0.253-0.5780.7350.735-0.3590.2060.129
Thinness 10-19 Years-0.034-0.6140.397-0.464-0.218-0.366-0.2280.4881.0000.943-0.577-0.5760.076-0.311-0.458-0.5480.6170.617-0.0730.3080.470
Thinness 5-9 Years-0.038-0.6250.408-0.459-0.229-0.383-0.2370.4700.9431.000-0.579-0.5810.094-0.301-0.473-0.5610.6210.621-0.0920.3220.475
Income Composition Of Resources0.1980.870-0.5620.5210.5140.2230.520-0.658-0.577-0.5791.0000.900-0.0080.6160.3330.614-0.906-0.9060.358-0.2320.709
Schooling0.1950.815-0.5070.5680.5120.2950.521-0.623-0.576-0.5810.9001.000-0.0460.5260.4390.607-0.884-0.8840.371-0.2960.647
Population0.050-0.0200.015-0.018-0.065-0.028-0.0580.0290.0760.094-0.008-0.0461.0000.687-0.021-0.2630.0720.072-0.1720.5530.178
GDP0.1930.559-0.3640.3440.2600.1070.290-0.387-0.311-0.3010.6160.5260.6871.0000.1770.232-0.541-0.5410.0640.2490.248
Percentage Expenditure0.0950.407-0.2520.4430.1940.6370.189-0.253-0.458-0.4730.3330.439-0.0210.1771.0000.284-0.415-0.4150.048-0.2310.433
BMI0.1870.592-0.3860.2850.3340.2460.345-0.578-0.548-0.5610.6140.607-0.2630.2320.2841.000-0.602-0.6020.284-0.4380.363
Infant Deaths-0.148-0.9270.600-0.548-0.591-0.300-0.6010.7350.6170.621-0.906-0.8840.072-0.541-0.415-0.6021.0001.000-0.4350.2990.548
Under-Five Deaths-0.148-0.9270.600-0.548-0.591-0.300-0.6010.7350.6170.621-0.906-0.8840.072-0.541-0.415-0.6021.0001.000-0.4350.2990.548
Hepatitis B0.0850.364-0.2380.1090.7700.0650.786-0.359-0.073-0.0920.3580.371-0.1720.0640.0480.284-0.435-0.4351.000-0.2500.177
Measles-0.110-0.2890.143-0.228-0.281-0.197-0.2740.2060.3080.322-0.232-0.2960.5530.249-0.231-0.4380.2990.299-0.2501.0000.000
Status0.0000.6270.3710.6750.3040.4150.3150.1290.4700.4750.7090.6470.1780.2480.4330.3630.5480.5480.1770.0001.000

Missing values

2023-01-20T23:01:13.048290image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-01-20T23:01:13.295823image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-01-20T23:01:13.547374image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

CountryYearStatusLife ExpectancyAdult MortalityAlcoholPolioTotal ExpenditureDiphtheriaHIV/AIDSThinness 10-19 YearsThinness 5-9 YearsIncome Composition Of ResourcesSchoolingPopulationGDPPercentage ExpenditureBMIInfant DeathsUnder-Five DeathsHepatitis BMeasles
1155Albania2007Developing75.99.05.5899.06.1098.00.11.61.70.70311.62970017.01.067732e+106.3251.93034815.315.398.022.0
937Uruguay2007Developing75.4124.06.6794.08.2394.00.11.61.50.76015.33328651.02.341057e+107.4953.19823411.011.094.00.0
973Guyana2005Developing65.0238.07.3593.05.8393.01.85.75.50.61911.4759709.08.248806e+083.7950.67011433.433.493.00.0
2081Cabo Verde2013Developing72.812.00.0193.04.2993.00.26.96.90.64313.6539940.01.850470e+095.2948.90684119.219.293.00.0
354Moldova2002Developing67.5225.06.7198.08.3197.00.13.43.6NaNNaN2911385.01.661818e+097.4052.34854821.721.799.04929.0
2570Netherlands2012Developed81.162.09.0597.011.1097.00.11.00.90.92118.116754962.08.389230e+1110.5451.3415483.63.620.010.0
1658Djibouti2004Developing58.1326.01.1864.06.7664.04.06.05.90.3883.7818373.06.660721e+083.3247.56978072.972.9NaN71.0
790Cameroon2004Developing52.1412.04.7172.04.7373.07.57.07.10.4558.216809407.01.882622e+104.0247.10416679.779.7NaN358.0
116DR Congo2015Developing59.8258.0NaN78.0NaN81.01.19.59.3NaNNaN78656904.03.791770e+103.9744.00164173.073.070.05020.0
12Slovakia2000Developed73.0147.011.0698.05.5099.00.11.61.70.76113.05388720.02.924256e+105.3051.3170978.28.298.00.0
CountryYearStatusLife ExpectancyAdult MortalityAlcoholPolioTotal ExpenditureDiphtheriaHIV/AIDSThinness 10-19 YearsThinness 5-9 YearsIncome Composition Of ResourcesSchoolingPopulationGDPPercentage ExpenditureBMIInfant DeathsUnder-Five DeathsHepatitis BMeasles
2686Chile2012Developing79.984.06.769.07.249.00.10.80.80.82615.517341771.02.671760e+117.0255.5895627.27.290.00.0
2161El Salvador2003Developing69.929.03.1093.07.6194.00.32.01.90.62812.06026849.01.324389e+108.4152.89423423.523.594.00.0
96St. Lucia2003Developing72.2171.012.5591.06.109.00.24.44.40.68412.4163047.09.874074e+085.2455.13578215.715.784.00.0
1761Uruguay2005Developing75.7123.06.3596.011.1596.00.11.61.60.75315.53317665.01.736286e+107.7652.82753112.512.596.00.0
1593Burkina Faso2010Developing57.5279.04.559.07.1791.01.09.08.60.3656.316116845.01.010962e+105.2443.67374168.168.191.02511.0
1147Central African Republic2002Developing45.658.01.4742.04.1644.013.41.21.20.3155.43930648.09.960682e+084.2343.459435108.5108.5NaN938.0
2154Serbia2003Developing73.0134.07.2589.08.1389.00.12.72.70.71513.07480591.02.248237e+108.1351.1236519.19.1NaN15.0
1766Estonia2005Developing72.8189.015.5296.05.2096.00.12.12.20.81215.91354775.01.410679e+105.0651.9932235.75.795.02.0
1122Guinea2010Developing57.8291.00.2062.04.5564.02.28.28.20.3808.310270728.06.853468e+093.4345.27392274.474.464.045.0
1346Uganda2006Developing54.941.09.8162.09.8664.08.66.46.40.43410.628773227.09.977648e+096.4643.27846061.661.664.05736.0